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Paper Abstract and Keywords
Presentation 2021-11-17 11:00
A study on variable step size diffusion LMS algorithm with deep unfolding
Tetsuya Sasaki (Osaka City Univ.), Kazunori Hayashi (Kyoto Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In adaptive filtering using a fixed step size LMS (Least-Mean-Square), there is a trade-off between convergence speed and steady-state error.
To solve the issue, a lot of variable step size algorithms have been proposed so far, but in many convetional approaches some parameters have to be adjusted heuristically to achieve good performance.
In this study, we propose a method named TDLMS (Trainable Diffusion LMS), which determines the step size in each iteration of the DLMS algorithm by data driven approach.
Numerical experiments show that the proposed TNLMS algorithm is superior to the existing variable step size NLMS algorithm under various conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep unfolding / Diffusion LMS algorithm / Variable step size / / / / /  
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Conference Information
Committee RISING  
Conference Date 2021-11-15 - 2021-11-17 
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Conference Code 2021-11-RISING 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on variable step size diffusion LMS algorithm with deep unfolding 
Sub Title (in English)  
Keyword(1) Deep unfolding  
Keyword(2) Diffusion LMS algorithm  
Keyword(3) Variable step size  
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1st Author's Name Tetsuya Sasaki  
1st Author's Affiliation Osaka City University (Osaka City Univ.)
2nd Author's Name Kazunori Hayashi  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2021-11-17 11:00:00 
Presentation Time 50 minutes 
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